Overview

Dataset statistics

Number of variables19
Number of observations9564
Missing cells4636
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory152.0 B

Variable types

Numeric16
Categorical3

Warnings

koi_limbdark_mod has constant value "Claret (2011 A&A 529 75) ATLAS LS" Constant
koi_period is highly correlated with koi_dorHigh correlation
koi_time0bk is highly correlated with koi_time0High correlation
koi_time0 is highly correlated with koi_time0bkHigh correlation
koi_impact is highly correlated with koi_rorHigh correlation
koi_ror is highly correlated with koi_impactHigh correlation
koi_dor is highly correlated with koi_periodHigh correlation
koi_ldm_coeff2 is highly correlated with koi_ldm_coeff1High correlation
koi_ldm_coeff1 is highly correlated with koi_ldm_coeff2High correlation
koi_parm_prov is highly correlated with koi_limbdark_modHigh correlation
koi_limbdark_mod is highly correlated with koi_parm_prov and 1 other fieldsHigh correlation
koi_fittype is highly correlated with koi_limbdark_modHigh correlation
koi_impact has 363 (3.8%) missing values Missing
koi_depth has 363 (3.8%) missing values Missing
koi_ror has 363 (3.8%) missing values Missing
koi_srho has 321 (3.4%) missing values Missing
koi_prad has 363 (3.8%) missing values Missing
koi_sma has 363 (3.8%) missing values Missing
koi_incl has 364 (3.8%) missing values Missing
koi_teq has 363 (3.8%) missing values Missing
koi_insol has 321 (3.4%) missing values Missing
koi_dor has 363 (3.8%) missing values Missing
koi_limbdark_mod has 363 (3.8%) missing values Missing
koi_ldm_coeff2 has 363 (3.8%) missing values Missing
koi_ldm_coeff1 has 363 (3.8%) missing values Missing
koi_period is highly skewed (γ1 = 96.4593262) Skewed
koi_impact is highly skewed (γ1 = 23.50594347) Skewed
koi_ror is highly skewed (γ1 = 23.96809624) Skewed
koi_prad is highly skewed (γ1 = 52.11895421) Skewed
koi_sma is highly skewed (γ1 = 54.10548219) Skewed
koi_insol is highly skewed (γ1 = 49.94777667) Skewed
koi_dor is highly skewed (γ1 = 90.61673425) Skewed
koi_period has unique values Unique

Reproduction

Analysis started2021-04-02 22:04:50.692672
Analysis finished2021-04-02 22:05:18.074236
Duration27.38 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

koi_period
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
UNIQUE

Distinct9564
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.67135842
Minimum0.241842544
Maximum129995.7784
Zeros0
Zeros (%)0.0%
Memory size74.8 KiB
2021-04-02T18:05:18.150032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.241842544
5-th percentile0.7055094771
Q12.733684197
median9.75283067
Q340.7151776
95-th percentile371.5625005
Maximum129995.7784
Range129995.5366
Interquartile range (IQR)37.9814934

Descriptive statistics

Standard deviation1334.744046
Coefficient of variation (CV)17.63869546
Kurtosis9389.730946
Mean75.67135842
Median Absolute Deviation (MAD)8.521484388
Skewness96.4593262
Sum723720.872
Variance1781541.668
MonotocityNot monotonic
2021-04-02T18:05:18.259712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225.262131
 
< 0.1%
0.9337594041
 
< 0.1%
103.3636911
 
< 0.1%
43.084101271
 
< 0.1%
27.254719931
 
< 0.1%
50.44674171
 
< 0.1%
5.063188031
 
< 0.1%
375.4089231
 
< 0.1%
1.6746908861
 
< 0.1%
0.7158258861
 
< 0.1%
Other values (9554)9554
99.9%
ValueCountFrequency (%)
0.2418425441
< 0.1%
0.2598196591
< 0.1%
0.2936300851
< 0.1%
0.2996977551
< 0.1%
0.3067024911
< 0.1%
ValueCountFrequency (%)
129995.77841
< 0.1%
2190.7010351
< 0.1%
1693.6636231
< 0.1%
1500.1406771
< 0.1%
15001
< 0.1%

koi_time0bk
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9538
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.1832508
Minimum120.5159138
Maximum1472.522306
Zeros0
Zeros (%)0.0%
Memory size74.8 KiB
2021-04-02T18:05:18.368449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum120.5159138
5-th percentile131.6994097
Q1132.7617175
median137.224595
Q3170.6946035
95-th percentile329.477865
Maximum1472.522306
Range1352.006392
Interquartile range (IQR)37.932886

Descriptive statistics

Standard deviation67.91895958
Coefficient of variation (CV)0.4086991875
Kurtosis25.00426527
Mean166.1832508
Median Absolute Deviation (MAD)5.364939
Skewness3.682070085
Sum1589376.611
Variance4612.98507
MonotocityNot monotonic
2021-04-02T18:05:18.480122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131.944992
 
< 0.1%
131.8952
 
< 0.1%
131.992392
 
< 0.1%
132.7622
 
< 0.1%
133.26122
 
< 0.1%
133.4042
 
< 0.1%
131.82112
 
< 0.1%
131.53372
 
< 0.1%
132.68912
 
< 0.1%
131.974922
 
< 0.1%
Other values (9528)9544
99.8%
ValueCountFrequency (%)
120.51591381
< 0.1%
120.56592541
< 0.1%
121.11942281
< 0.1%
121.2937711
< 0.1%
121.35854171
< 0.1%
ValueCountFrequency (%)
1472.5223061
< 0.1%
907.044711
< 0.1%
801.45520051
< 0.1%
746.19676761
< 0.1%
657.268771
< 0.1%

koi_time0
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7445
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2454999.183
Minimum2454953.516
Maximum2456305.522
Zeros0
Zeros (%)0.0%
Memory size74.8 KiB
2021-04-02T18:05:18.591852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2454953.516
5-th percentile2454964.699
Q12454965.762
median2454970.225
Q32455003.695
95-th percentile2455162.478
Maximum2456305.522
Range1352.006
Interquartile range (IQR)37.93275

Descriptive statistics

Standard deviation67.91896004
Coefficient of variation (CV)2.766557338 × 105
Kurtosis25.00425718
Mean2454999.183
Median Absolute Deviation (MAD)5.365
Skewness3.682070068
Sum2.347961219 × 1010
Variance4612.985133
MonotocityNot monotonic
2021-04-02T18:05:18.712500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2454964.81710
 
0.1%
2454964.8038
 
0.1%
2454964.7978
 
0.1%
2454964.8957
 
0.1%
2454965.1237
 
0.1%
2454965.17
 
0.1%
2454964.8317
 
0.1%
2454964.7967
 
0.1%
2454964.9917
 
0.1%
2454964.7567
 
0.1%
Other values (7435)9489
99.2%
ValueCountFrequency (%)
2454953.5161
< 0.1%
2454953.5661
< 0.1%
2454954.1191
< 0.1%
2454954.2941
< 0.1%
2454954.3591
< 0.1%
ValueCountFrequency (%)
2456305.5221
< 0.1%
2455740.0451
< 0.1%
2455634.4551
< 0.1%
2455579.1971
< 0.1%
2455490.2691
< 0.1%

koi_impact
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct2406
Distinct (%)26.1%
Missing363
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean0.7351054777
Minimum0
Maximum100.806
Zeros15
Zeros (%)0.2%
Memory size74.8 KiB
2021-04-02T18:05:18.831203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.017
Q10.197
median0.537
Q30.889
95-th percentile1.251
Maximum100.806
Range100.806
Interquartile range (IQR)0.692

Descriptive statistics

Standard deviation3.34883202
Coefficient of variation (CV)4.555580284
Kurtosis599.0571169
Mean0.7351054777
Median Absolute Deviation (MAD)0.345
Skewness23.50594347
Sum6763.7055
Variance11.2146759
MonotocityNot monotonic
2021-04-02T18:05:18.933935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00137
 
0.4%
0.02536
 
0.4%
0.00435
 
0.4%
0.02232
 
0.3%
0.01731
 
0.3%
0.01528
 
0.3%
0.01328
 
0.3%
0.00828
 
0.3%
0.01927
 
0.3%
0.02126
 
0.3%
Other values (2396)8893
93.0%
(Missing)363
 
3.8%
ValueCountFrequency (%)
015
0.2%
0.00061
 
< 0.1%
0.00137
0.4%
0.00111
 
< 0.1%
0.00121
 
< 0.1%
ValueCountFrequency (%)
100.8061
< 0.1%
100.1961
< 0.1%
99.851
< 0.1%
98.60211
< 0.1%
88.72431
< 0.1%

koi_duration
Real number (ℝ≥0)

Distinct7834
Distinct (%)81.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.621606392
Minimum0.052
Maximum138.54
Zeros0
Zeros (%)0.0%
Memory size74.8 KiB
2021-04-02T18:05:19.036654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.052
5-th percentile1.252135
Q12.43775
median3.7926
Q36.2765
95-th percentile16.027
Maximum138.54
Range138.488
Interquartile range (IQR)3.83875

Descriptive statistics

Standard deviation6.471553737
Coefficient of variation (CV)1.151192966
Kurtosis64.12499518
Mean5.621606392
Median Absolute Deviation (MAD)1.6576
Skewness5.928764864
Sum53765.04353
Variance41.88100777
MonotocityNot monotonic
2021-04-02T18:05:19.138397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.156
 
0.1%
2.096
 
0.1%
2.716
 
0.1%
26
 
0.1%
1.556
 
0.1%
2.9525
 
0.1%
2.7525
 
0.1%
1.6835
 
0.1%
2.4695
 
0.1%
1.224
 
< 0.1%
Other values (7824)9510
99.4%
ValueCountFrequency (%)
0.0521
< 0.1%
0.10461
< 0.1%
0.1671
< 0.1%
0.18661
< 0.1%
0.2221
< 0.1%
ValueCountFrequency (%)
138.541
< 0.1%
117.521
< 0.1%
90.951
< 0.1%
90.011
< 0.1%
88.11181
< 0.1%

koi_depth
Real number (ℝ≥0)

MISSING

Distinct2853
Distinct (%)31.0%
Missing363
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean23792.45353
Minimum0
Maximum1540000
Zeros1
Zeros (%)< 0.1%
Memory size74.8 KiB
2021-04-02T18:05:19.253118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47.6
Q1160
median421
Q31470
95-th percentile172000
Maximum1540000
Range1540000
Interquartile range (IQR)1310

Descriptive statistics

Standard deviation82243.16102
Coefficient of variation (CV)3.456691044
Kurtosis38.4731925
Mean23792.45353
Median Absolute Deviation (MAD)327.4
Skewness5.259032492
Sum218914364.9
Variance6763937535
MonotocityNot monotonic
2021-04-02T18:05:19.365817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13425
 
0.3%
11625
 
0.3%
12623
 
0.2%
103022
 
0.2%
11722
 
0.2%
111022
 
0.2%
104022
 
0.2%
12022
 
0.2%
11521
 
0.2%
102021
 
0.2%
Other values (2843)8976
93.9%
(Missing)363
 
3.8%
ValueCountFrequency (%)
01
< 0.1%
0.81
< 0.1%
1.71
< 0.1%
4.51
< 0.1%
5.91
< 0.1%
ValueCountFrequency (%)
15400001
< 0.1%
9220001
< 0.1%
9070001
< 0.1%
9020001
< 0.1%
8940001
< 0.1%

koi_ror
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct8502
Distinct (%)92.4%
Missing363
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean0.2836462862
Minimum0.001289
Maximum99.870651
Zeros0
Zeros (%)0.0%
Memory size74.8 KiB
2021-04-02T18:05:19.490477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.001289
5-th percentile0.006575
Q10.012341
median0.021076
Q30.095348
95-th percentile0.552011
Maximum99.870651
Range99.869362
Interquartile range (IQR)0.083007

Descriptive statistics

Standard deviation3.306558165
Coefficient of variation (CV)11.65732931
Kurtosis615.5176723
Mean0.2836462862
Median Absolute Deviation (MAD)0.011301
Skewness23.96809624
Sum2609.829479
Variance10.9333269
MonotocityNot monotonic
2021-04-02T18:05:19.594207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0098544
 
< 0.1%
0.0298434
 
< 0.1%
0.0090134
 
< 0.1%
0.0104273
 
< 0.1%
0.0127083
 
< 0.1%
0.0135643
 
< 0.1%
0.012523
 
< 0.1%
0.0296073
 
< 0.1%
0.0236453
 
< 0.1%
0.0108123
 
< 0.1%
Other values (8492)9168
95.9%
(Missing)363
 
3.8%
ValueCountFrequency (%)
0.0012891
< 0.1%
0.0018811
< 0.1%
0.0020731
< 0.1%
0.0022841
< 0.1%
0.0025161
< 0.1%
ValueCountFrequency (%)
99.8706511
< 0.1%
99.2306061
< 0.1%
98.8803861
< 0.1%
97.6051561
< 0.1%
87.7516171
< 0.1%

koi_srho
Real number (ℝ≥0)

MISSING

Distinct9002
Distinct (%)97.4%
Missing321
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean9.164413809
Minimum4 × 105
Maximum980.85419
Zeros0
Zeros (%)0.0%
Memory size74.8 KiB
2021-04-02T18:05:19.701920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4 × 105
5-th percentile0.015551
Q10.22925
median0.95672
Q32.897175
95-th percentile28.034941
Maximum980.85419
Range980.85415
Interquartile range (IQR)2.667925

Descriptive statistics

Standard deviation53.80796733
Coefficient of variation (CV)5.871403065
Kurtosis179.7338724
Mean9.164413809
Median Absolute Deviation (MAD)0.86919
Skewness12.56040954
Sum84706.67684
Variance2895.297348
MonotocityNot monotonic
2021-04-02T18:05:19.805643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.060714
 
< 0.1%
0.00183
 
< 0.1%
0.087533
 
< 0.1%
0.001023
 
< 0.1%
0.000373
 
< 0.1%
0.00023
 
< 0.1%
0.000113
 
< 0.1%
0.021643
 
< 0.1%
0.02413
 
< 0.1%
2.863433
 
< 0.1%
Other values (8992)9212
96.3%
(Missing)321
 
3.4%
ValueCountFrequency (%)
4 × 1052
< 0.1%
6 × 1051
< 0.1%
7 × 1051
< 0.1%
8 × 1051
< 0.1%
9 × 1052
< 0.1%
ValueCountFrequency (%)
980.854191
< 0.1%
976.101321
< 0.1%
968.920841
< 0.1%
959.132391
< 0.1%
950.095151
< 0.1%

koi_fittype
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size74.8 KiB
LS+MCMC
7897 
MCMC
1206 
none
 
369
LS
 
92

Length

Max length7
Median length7
Mean length6.457862819
Min length2

Characters and Unicode

Total characters61763
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLS+MCMC
2nd rowLS+MCMC
3rd rowLS+MCMC
4th rowLS+MCMC
5th rowLS+MCMC
ValueCountFrequency (%)
LS+MCMC7897
82.6%
MCMC1206
 
12.6%
none369
 
3.9%
LS92
 
1.0%
2021-04-02T18:05:20.002086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-02T18:05:20.065916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
ls+mcmc7897
82.6%
mcmc1206
 
12.6%
none369
 
3.9%
ls92
 
1.0%

Most occurring characters

ValueCountFrequency (%)
M18206
29.5%
C18206
29.5%
L7989
12.9%
S7989
12.9%
+7897
12.8%
n738
 
1.2%
o369
 
0.6%
e369
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter52390
84.8%
Math Symbol7897
 
12.8%
Lowercase Letter1476
 
2.4%

Most frequent character per category

ValueCountFrequency (%)
M18206
34.8%
C18206
34.8%
L7989
15.2%
S7989
15.2%
ValueCountFrequency (%)
n738
50.0%
o369
25.0%
e369
25.0%
ValueCountFrequency (%)
+7897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin53866
87.2%
Common7897
 
12.8%

Most frequent character per script

ValueCountFrequency (%)
M18206
33.8%
C18206
33.8%
L7989
14.8%
S7989
14.8%
n738
 
1.4%
o369
 
0.7%
e369
 
0.7%
ValueCountFrequency (%)
+7897
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII61763
100.0%

Most frequent character per block

ValueCountFrequency (%)
M18206
29.5%
C18206
29.5%
L7989
12.9%
S7989
12.9%
+7897
12.8%
n738
 
1.2%
o369
 
0.6%
e369
 
0.6%

koi_prad
Real number (ℝ≥0)

MISSING
SKEWED

Distinct2988
Distinct (%)32.5%
Missing363
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean102.8917781
Minimum0.08
Maximum200346
Zeros0
Zeros (%)0.0%
Memory size74.8 KiB
2021-04-02T18:05:20.155675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.76
Q11.4
median2.39
Q314.93
95-th percentile87.21
Maximum200346
Range200345.92
Interquartile range (IQR)13.53

Descriptive statistics

Standard deviation3077.639126
Coefficient of variation (CV)29.9114194
Kurtosis2974.910711
Mean102.8917781
Median Absolute Deviation (MAD)1.31
Skewness52.11895421
Sum946707.25
Variance9471862.591
MonotocityNot monotonic
2021-04-02T18:05:20.258400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0839
 
0.4%
1.2139
 
0.4%
1.1939
 
0.4%
1.1838
 
0.4%
1.537
 
0.4%
1.1436
 
0.4%
1.3936
 
0.4%
1.1236
 
0.4%
1.5736
 
0.4%
1.335
 
0.4%
Other values (2978)8830
92.3%
(Missing)363
 
3.8%
ValueCountFrequency (%)
0.081
< 0.1%
0.141
< 0.1%
0.181
< 0.1%
0.191
< 0.1%
0.221
< 0.1%
ValueCountFrequency (%)
2003461
< 0.1%
1618581
< 0.1%
1090611
< 0.1%
64333.81
< 0.1%
46743.41
< 0.1%

koi_sma
Real number (ℝ≥0)

MISSING
SKEWED

Distinct3796
Distinct (%)41.3%
Missing363
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean0.2239887404
Minimum0.0059
Maximum44.9892
Zeros0
Zeros (%)0.0%
Memory size74.8 KiB
2021-04-02T18:05:20.372127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.0059
5-th percentile0.0152
Q10.0377
median0.0851
Q30.2144
95-th percentile1.0187
Maximum44.9892
Range44.9833
Interquartile range (IQR)0.1767

Descriptive statistics

Standard deviation0.5663594025
Coefficient of variation (CV)2.528517289
Kurtosis4243.011046
Mean0.2239887404
Median Absolute Deviation (MAD)0.0589
Skewness54.10548219
Sum2060.9204
Variance0.3207629729
MonotocityNot monotonic
2021-04-02T18:05:20.474851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.020321
 
0.2%
0.013620
 
0.2%
0.016318
 
0.2%
0.012817
 
0.2%
0.015317
 
0.2%
0.014716
 
0.2%
0.027616
 
0.2%
0.013416
 
0.2%
0.024116
 
0.2%
0.015515
 
0.2%
Other values (3786)9029
94.4%
(Missing)363
 
3.8%
ValueCountFrequency (%)
0.00591
< 0.1%
0.00652
< 0.1%
0.00721
< 0.1%
0.00731
< 0.1%
0.0082
< 0.1%
ValueCountFrequency (%)
44.98921
< 0.1%
2.94561
< 0.1%
2.83641
< 0.1%
2.6341
< 0.1%
2.41441
< 0.1%

koi_incl
Real number (ℝ≥0)

MISSING

Distinct2260
Distinct (%)24.6%
Missing364
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean82.46914674
Minimum2.29
Maximum90
Zeros0
Zeros (%)0.0%
Memory size74.8 KiB
2021-04-02T18:05:20.586551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.29
5-th percentile46.4145
Q183.92
median88.5
Q389.77
95-th percentile89.97
Maximum90
Range87.71
Interquartile range (IQR)5.85

Descriptive statistics

Standard deviation15.22362696
Coefficient of variation (CV)0.1845978473
Kurtosis9.247774892
Mean82.46914674
Median Absolute Deviation (MAD)1.45
Skewness-3.04344561
Sum758716.15
Variance231.7588178
MonotocityNot monotonic
2021-04-02T18:05:20.684283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89.95879
 
9.2%
89.99181
 
1.9%
89.98131
 
1.4%
89.38130
 
1.4%
90123
 
1.3%
89.97109
 
1.1%
89.9686
 
0.9%
89.9386
 
0.9%
89.9472
 
0.8%
88.8162
 
0.6%
Other values (2250)7341
76.8%
(Missing)364
 
3.8%
ValueCountFrequency (%)
2.291
< 0.1%
5.261
< 0.1%
5.581
< 0.1%
5.731
< 0.1%
6.711
< 0.1%
ValueCountFrequency (%)
90123
1.3%
89.99181
1.9%
89.98131
1.4%
89.97109
1.1%
89.9686
0.9%

koi_teq
Real number (ℝ≥0)

MISSING

Distinct2511
Distinct (%)27.3%
Missing363
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean1085.385828
Minimum25
Maximum14667
Zeros0
Zeros (%)0.0%
Memory size74.8 KiB
2021-04-02T18:05:20.788012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile256
Q1539
median878
Q31379
95-th percentile2545
Maximum14667
Range14642
Interquartile range (IQR)840

Descriptive statistics

Standard deviation856.3511615
Coefficient of variation (CV)0.7889831797
Kurtosis27.68759662
Mean1085.385828
Median Absolute Deviation (MAD)388
Skewness3.505694051
Sum9986635
Variance733337.3118
MonotocityNot monotonic
2021-04-02T18:05:20.894726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73414
 
0.1%
52314
 
0.1%
53014
 
0.1%
72914
 
0.1%
55414
 
0.1%
56813
 
0.1%
32113
 
0.1%
50313
 
0.1%
67613
 
0.1%
83013
 
0.1%
Other values (2501)9066
94.8%
(Missing)363
 
3.8%
ValueCountFrequency (%)
251
< 0.1%
921
< 0.1%
941
< 0.1%
1011
< 0.1%
1061
< 0.1%
ValueCountFrequency (%)
146671
< 0.1%
131841
< 0.1%
118041
< 0.1%
106341
< 0.1%
103401
< 0.1%

koi_insol
Real number (ℝ≥0)

MISSING
SKEWED

Distinct7801
Distinct (%)84.4%
Missing321
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean7745.737023
Minimum0
Maximum10947554.55
Zeros1
Zeros (%)< 0.1%
Memory size74.8 KiB
2021-04-02T18:05:21.230868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.021
Q120.15
median141.6
Q3870.29
95-th percentile10018.065
Maximum10947554.55
Range10947554.55
Interquartile range (IQR)850.14

Descriptive statistics

Standard deviation159204.6652
Coefficient of variation (CV)20.55384333
Kurtosis2965.678172
Mean7745.737023
Median Absolute Deviation (MAD)138.95
Skewness49.94777667
Sum71593847.3
Variance2.534612542 × 1010
MonotocityNot monotonic
2021-04-02T18:05:21.335589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3511
 
0.1%
1.2811
 
0.1%
0.5210
 
0.1%
0.5810
 
0.1%
0.459
 
0.1%
0.479
 
0.1%
0.519
 
0.1%
0.749
 
0.1%
0.89
 
0.1%
0.819
 
0.1%
Other values (7791)9147
95.6%
(Missing)321
 
3.4%
ValueCountFrequency (%)
01
 
< 0.1%
0.022
< 0.1%
0.034
< 0.1%
0.042
< 0.1%
0.061
 
< 0.1%
ValueCountFrequency (%)
10947554.551
< 0.1%
7165673.121
< 0.1%
4601339.961
< 0.1%
3036857.561
< 0.1%
2710661.91
< 0.1%

koi_dor
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct6210
Distinct (%)67.5%
Missing363
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean76.73633334
Minimum0.373
Maximum79614
Zeros0
Zeros (%)0.0%
Memory size74.8 KiB
2021-04-02T18:05:21.447311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.373
5-th percentile1.46
Q15.358
median15.46
Q345.37
95-th percentile321.6
Maximum79614
Range79613.627
Interquartile range (IQR)40.012

Descriptive statistics

Standard deviation845.2745977
Coefficient of variation (CV)11.01531127
Kurtosis8523.872348
Mean76.73633334
Median Absolute Deviation (MAD)12.408
Skewness90.61673425
Sum706051.0031
Variance714489.1455
MonotocityNot monotonic
2021-04-02T18:05:21.553035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.210
 
0.1%
23.29
 
0.1%
24.79
 
0.1%
6.199
 
0.1%
258
 
0.1%
21.28
 
0.1%
228
 
0.1%
21.68
 
0.1%
287
 
0.1%
26.67
 
0.1%
Other values (6200)9118
95.3%
(Missing)363
 
3.8%
ValueCountFrequency (%)
0.3731
< 0.1%
0.3861
< 0.1%
0.561
< 0.1%
0.95151
< 0.1%
0.9771
< 0.1%
ValueCountFrequency (%)
796141
< 0.1%
2421.71
< 0.1%
22731
< 0.1%
21881
< 0.1%
2162.91
< 0.1%

koi_limbdark_mod
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing363
Missing (%)3.8%
Memory size74.8 KiB
Claret (2011 A&A 529 75) ATLAS LS
9201 

Length

Max length33
Median length33
Mean length33
Min length33

Characters and Unicode

Total characters303633
Distinct characters20
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClaret (2011 A&A 529 75) ATLAS LS
2nd rowClaret (2011 A&A 529 75) ATLAS LS
3rd rowClaret (2011 A&A 529 75) ATLAS LS
4th rowClaret (2011 A&A 529 75) ATLAS LS
5th rowClaret (2011 A&A 529 75) ATLAS LS
ValueCountFrequency (%)
Claret (2011 A&A 529 75) ATLAS LS9201
96.2%
(Missing)363
 
3.8%
2021-04-02T18:05:21.732539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-02T18:05:21.784388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
atlas9201
14.3%
ls9201
14.3%
a&a9201
14.3%
5299201
14.3%
claret9201
14.3%
20119201
14.3%
759201
14.3%

Most occurring characters

ValueCountFrequency (%)
55206
18.2%
A36804
 
12.1%
218402
 
6.1%
118402
 
6.1%
518402
 
6.1%
L18402
 
6.1%
S18402
 
6.1%
C9201
 
3.0%
l9201
 
3.0%
a9201
 
3.0%
Other values (10)92010
30.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter92010
30.3%
Decimal Number82809
27.3%
Space Separator55206
18.2%
Lowercase Letter46005
15.2%
Open Punctuation9201
 
3.0%
Other Punctuation9201
 
3.0%
Close Punctuation9201
 
3.0%

Most frequent character per category

ValueCountFrequency (%)
218402
22.2%
118402
22.2%
518402
22.2%
09201
11.1%
99201
11.1%
79201
11.1%
ValueCountFrequency (%)
A36804
40.0%
L18402
20.0%
S18402
20.0%
C9201
 
10.0%
T9201
 
10.0%
ValueCountFrequency (%)
l9201
20.0%
a9201
20.0%
r9201
20.0%
e9201
20.0%
t9201
20.0%
ValueCountFrequency (%)
55206
100.0%
ValueCountFrequency (%)
(9201
100.0%
ValueCountFrequency (%)
&9201
100.0%
ValueCountFrequency (%)
)9201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common165618
54.5%
Latin138015
45.5%

Most frequent character per script

ValueCountFrequency (%)
A36804
26.7%
L18402
13.3%
S18402
13.3%
C9201
 
6.7%
l9201
 
6.7%
a9201
 
6.7%
r9201
 
6.7%
e9201
 
6.7%
t9201
 
6.7%
T9201
 
6.7%
ValueCountFrequency (%)
55206
33.3%
218402
 
11.1%
118402
 
11.1%
518402
 
11.1%
(9201
 
5.6%
09201
 
5.6%
&9201
 
5.6%
99201
 
5.6%
79201
 
5.6%
)9201
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII303633
100.0%

Most frequent character per block

ValueCountFrequency (%)
55206
18.2%
A36804
 
12.1%
218402
 
6.1%
118402
 
6.1%
518402
 
6.1%
L18402
 
6.1%
S18402
 
6.1%
C9201
 
3.0%
l9201
 
3.0%
a9201
 
3.0%
Other values (10)92010
30.3%

koi_ldm_coeff2
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1080
Distinct (%)11.7%
Missing363
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean0.2544391044
Minimum-0.1206
Maximum0.4822
Zeros0
Zeros (%)0.0%
Memory size74.8 KiB
2021-04-02T18:05:21.845225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.1206
5-th percentile0.1171
Q10.2286
median0.2711
Q30.2998
95-th percentile0.318
Maximum0.4822
Range0.6028
Interquartile range (IQR)0.0712

Descriptive statistics

Standard deviation0.06480594611
Coefficient of variation (CV)0.2547012035
Kurtosis2.505361272
Mean0.2544391044
Median Absolute Deviation (MAD)0.0318
Skewness-1.218318243
Sum2341.0942
Variance0.004199810651
MonotocityNot monotonic
2021-04-02T18:05:21.947968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2622251
 
2.6%
0.2889246
 
2.6%
0.2676207
 
2.2%
0.2916194
 
2.0%
0.2787185
 
1.9%
0.302170
 
1.8%
0.2602167
 
1.7%
0.2291161
 
1.7%
0.2965156
 
1.6%
0.2865145
 
1.5%
Other values (1070)7319
76.5%
(Missing)363
 
3.8%
ValueCountFrequency (%)
-0.12063
< 0.1%
-0.12041
 
< 0.1%
-0.11981
 
< 0.1%
-0.11931
 
< 0.1%
-0.11581
 
< 0.1%
ValueCountFrequency (%)
0.48221
 
< 0.1%
0.4642
 
< 0.1%
0.45151
 
< 0.1%
0.44671
 
< 0.1%
0.441815
0.2%

koi_ldm_coeff1
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1322
Distinct (%)14.4%
Missing363
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean0.4076165417
Minimum0.1254
Maximum0.9486
Zeros0
Zeros (%)0.0%
Memory size74.8 KiB
2021-04-02T18:05:22.061646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.1254
5-th percentile0.2736
Q10.3268
median0.392
Q30.4641
95-th percentile0.6188
Maximum0.9486
Range0.8232
Interquartile range (IQR)0.1373

Descriptive statistics

Standard deviation0.1060755203
Coefficient of variation (CV)0.2602336006
Kurtosis0.9216967819
Mean0.4076165417
Median Absolute Deviation (MAD)0.0682
Skewness0.9110793731
Sum3750.4798
Variance0.011252016
MonotocityNot monotonic
2021-04-02T18:05:22.160411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4035250
 
2.6%
0.3511244
 
2.6%
0.3928207
 
2.2%
0.3447196
 
2.0%
0.319169
 
1.8%
0.4096165
 
1.7%
0.4603160
 
1.7%
0.3342154
 
1.6%
0.3728148
 
1.5%
0.4526143
 
1.5%
Other values (1312)7365
77.0%
(Missing)363
 
3.8%
ValueCountFrequency (%)
0.12541
< 0.1%
0.12781
< 0.1%
0.15042
< 0.1%
0.1632
< 0.1%
0.16311
< 0.1%
ValueCountFrequency (%)
0.94861
 
< 0.1%
0.94431
 
< 0.1%
0.94281
 
< 0.1%
0.93893
< 0.1%
0.93861
 
< 0.1%

koi_parm_prov
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size74.8 KiB
q1_q17_dr25_koi
8054 
q1_q16_koi
1142 
q1_q17_dr24_koi
 
368

Length

Max length15
Median length15
Mean length14.40296947
Min length10

Characters and Unicode

Total characters137750
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowq1_q17_dr25_koi
2nd rowq1_q17_dr25_koi
3rd rowq1_q17_dr25_koi
4th rowq1_q17_dr25_koi
5th rowq1_q17_dr25_koi
ValueCountFrequency (%)
q1_q17_dr25_koi8054
84.2%
q1_q16_koi1142
 
11.9%
q1_q17_dr24_koi368
 
3.8%
2021-04-02T18:05:22.371845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-04-02T18:05:22.433679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
q1_q17_dr25_koi8054
84.2%
q1_q16_koi1142
 
11.9%
q1_q17_dr24_koi368
 
3.8%

Most occurring characters

ValueCountFrequency (%)
_27550
20.0%
q19128
13.9%
119128
13.9%
k9564
 
6.9%
o9564
 
6.9%
i9564
 
6.9%
78422
 
6.1%
d8422
 
6.1%
r8422
 
6.1%
28422
 
6.1%
Other values (3)9564
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter64664
46.9%
Decimal Number45536
33.1%
Connector Punctuation27550
20.0%

Most frequent character per category

ValueCountFrequency (%)
q19128
29.6%
k9564
14.8%
o9564
14.8%
i9564
14.8%
d8422
13.0%
r8422
13.0%
ValueCountFrequency (%)
119128
42.0%
78422
18.5%
28422
18.5%
58054
17.7%
61142
 
2.5%
4368
 
0.8%
ValueCountFrequency (%)
_27550
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common73086
53.1%
Latin64664
46.9%

Most frequent character per script

ValueCountFrequency (%)
_27550
37.7%
119128
26.2%
78422
 
11.5%
28422
 
11.5%
58054
 
11.0%
61142
 
1.6%
4368
 
0.5%
ValueCountFrequency (%)
q19128
29.6%
k9564
14.8%
o9564
14.8%
i9564
14.8%
d8422
13.0%
r8422
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII137750
100.0%

Most frequent character per block

ValueCountFrequency (%)
_27550
20.0%
q19128
13.9%
119128
13.9%
k9564
 
6.9%
o9564
 
6.9%
i9564
 
6.9%
78422
 
6.1%
d8422
 
6.1%
r8422
 
6.1%
28422
 
6.1%
Other values (3)9564
 
6.9%

Interactions

2021-04-02T18:04:52.593586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:52.704289image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:52.806017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:52.899767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:53.000525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:53.108237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:53.201986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:53.354549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:53.460267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:53.553018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:53.644773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:53.745503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:53.842244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:53.946964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:54.044724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:54.135488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:54.235223image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:54.336948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:54.432692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:54.533416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:54.637117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:54.731864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:54.824615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:54.929365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:55.022115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:55.114866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:55.216597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:55.315331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:55.421048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:55.521994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:55.615746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:55.720463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:55.826183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:55.927880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:56.035613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:56.219128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:56.320856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:56.422577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:56.536274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:56.635016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:56.734750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:56.842460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:56.946159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:57.059880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:57.163601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:57.258348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:57.355094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:57.449835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:57.549562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:57.647307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:57.751029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:57.845748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:57.938500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:58.040255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:58.132012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:58.220744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:58.317485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:58.411235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:58.510967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:58.608734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:58.695474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:58.796204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:58.896935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:59.002652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:59.101388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:59.211094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:59.308832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:59.407568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:59.515280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:59.610027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:59.792538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:59.898255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:04:59.999983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:00.109689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:00.212415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:00.307191image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:00.419888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:00.528590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:00.641294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:00.748987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:00.859710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:00.965429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:01.071145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:01.188830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:01.292553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:01.396274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:01.511937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:01.622669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:01.737334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:01.844079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:01.943782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:02.037559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:02.130310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:02.227054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:02.316812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:02.413555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:02.517247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:02.608032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:02.707766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:02.794535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:02.883295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:02.981034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:03.080760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:03.183465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:03.279225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:03.367002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:03.464740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:03.561485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:03.660217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:03.751971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:03.848712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:03.953433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:04.156889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:04.255624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:04.343388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:04.431154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:04.526869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:04.619649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:04.721377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:04.817120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:04.901896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:05.009578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:05.116292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:05.225001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:05.327726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:05.434441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:05.550131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:05.652886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:05.758602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:05.861327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:05.964052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:06.076751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:06.182439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:06.294140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:06.401852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:06.500619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:06.594368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:06.686124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:06.780865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:06.868634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:06.963371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:07.065098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:07.153869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:07.241632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:07.340369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:07.425142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:07.518892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:07.609651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:07.705392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:07.797146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:07.880923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:07.974671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:08.067416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:08.164164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:08.252927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:08.347675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:08.448404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:08.536140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:08.625929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:08.725662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:08.811431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:08.904156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:08.992918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:09.091654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:09.182411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:09.392848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:09.498595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:09.604310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:09.711030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:09.811749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:09.916475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:10.025177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:10.123923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:10.222656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:10.333362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:10.432068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:10.529834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:10.631562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:10.746255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:10.847982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:10.940736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:11.039447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:11.138178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:11.237939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:11.334680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:11.432421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:11.538130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:11.632884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:11.725606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:11.829328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:11.918091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:12.007850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:12.110605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:12.213328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:12.311067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:12.400828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:12.507543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:12.613261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:12.721965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:12.823695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:12.932407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:13.047070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:13.152815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:13.258532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:13.374222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:13.475487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:13.579181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:13.689913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:13.795602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:13.904340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:14.003074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:14.105800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:14.208524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:14.314241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:14.412980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:14.518688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:14.629371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:14.728106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:14.823849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:14.929595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:15.024344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:15.119087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:15.220788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:15.322543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:15.427235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:15.518989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:15.607752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:15.696514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:15.944850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:16.031617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:16.124369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:16.219116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:16.303889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:16.388690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:16.482439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:16.566215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:16.648996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:16.740747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:16.827518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-02T18:05:16.922262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-04-02T18:05:22.512440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-02T18:05:22.705950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-02T18:05:22.900424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-02T18:05:23.095892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-02T18:05:23.275398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-02T18:05:17.106771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-02T18:05:17.398959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-04-02T18:05:17.640368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-04-02T18:05:17.947575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

koi_periodkoi_time0bkkoi_time0koi_impactkoi_durationkoi_depthkoi_rorkoi_srhokoi_fittypekoi_pradkoi_smakoi_inclkoi_teqkoi_insolkoi_dorkoi_limbdark_modkoi_ldm_coeff2koi_ldm_coeff1koi_parm_prov
09.488036170.5387502455003.5390.1462.95750616.00.0223443.20796LS+MCMC2.260.085389.66793.093.5924.810Claret (2011 A&A 529 75) ATLAS LS0.22910.4603q1_q17_dr25_koi
154.418383162.5138402454995.5140.5864.50700875.00.0279543.02368LS+MCMC2.830.273489.57443.09.1177.900Claret (2011 A&A 529 75) ATLAS LS0.22910.4603q1_q17_dr25_koi
219.899140175.8502522455008.8500.9691.7822010800.00.1540467.29555LS+MCMC14.600.141988.96638.039.3053.500Claret (2011 A&A 529 75) ATLAS LS0.27110.3858q1_q17_dr25_koi
31.736952170.3075652455003.3081.2762.406418080.00.3873940.22080LS+MCMC33.460.026767.091395.0891.963.278Claret (2011 A&A 529 75) ATLAS LS0.28650.3556q1_q17_dr25_koi
42.525592171.5955502455004.5960.7011.65450603.00.0240641.98635LS+MCMC2.750.037485.411406.0926.168.750Claret (2011 A&A 529 75) ATLAS LS0.28440.3661q1_q17_dr25_koi
511.094321171.2011602455004.2010.5384.594501520.00.0367790.67324LS+MCMC3.900.099288.11835.0114.8116.360Claret (2011 A&A 529 75) ATLAS LS0.28890.3511q1_q17_dr25_koi
64.134435172.9793702455005.9790.7623.14020686.00.0261330.37377LS+MCMC2.770.051483.721160.0427.656.960Claret (2011 A&A 529 75) ATLAS LS0.28890.3511q1_q17_dr25_koi
72.566589179.5543702455012.5540.7552.42900227.00.0149830.48909LS+MCMC1.590.037482.171360.0807.745.540Claret (2011 A&A 529 75) ATLAS LS0.28890.3511q1_q17_dr25_koi
87.361790132.2505302454965.2511.1695.02200234.00.1833870.00485LS+MCMC39.210.082060.921342.0767.222.400Claret (2011 A&A 529 75) ATLAS LS0.30500.3201q1_q17_dr25_koi
916.068647173.6219372455006.6220.0523.534704910.00.0621613.66590LS+MCMC5.760.115889.92600.030.7536.850Claret (2011 A&A 529 75) ATLAS LS0.14510.5820q1_q17_dr25_koi

Last rows

koi_periodkoi_time0bkkoi_time0koi_impactkoi_durationkoi_depthkoi_rorkoi_srhokoi_fittypekoi_pradkoi_smakoi_inclkoi_teqkoi_insolkoi_dorkoi_limbdark_modkoi_ldm_coeff2koi_ldm_coeff1koi_parm_prov
9554330.808790281.1568002455114.1570.92021.6690275.00.0182860.02293LS+MCMC2.130.936388.97290.01.6751.000Claret (2011 A&A 529 75) ATLAS LS0.30320.3147q1_q17_dr25_koi
9555522.646130190.3389002455023.3390.00717.7700648.00.0229420.84129LS+MCMC0.800.869590.0092.00.02230.000Claret (2011 A&A 529 75) ATLAS LS0.37750.3402q1_q17_dr25_koi
9556392.942470383.6730002455216.6730.01011.0550120.00.0101462.52821LS+MCMC1.601.012390.00347.03.43274.000Claret (2011 A&A 529 75) ATLAS LS0.29980.2903q1_q17_dr25_koi
9557373.893980261.4968002455094.4970.96327.6600730.00.0328780.00771LS+MCMC2.510.888588.57206.00.4238.500Claret (2011 A&A 529 75) ATLAS LS0.23370.4535q1_q17_dr25_koi
95588.589871132.0161002454965.0160.7654.806087.70.0093640.18863LS+MCMC1.110.077985.14929.0176.409.030Claret (2011 A&A 529 75) ATLAS LS0.27110.3858q1_q17_dr25_koi
95590.527699131.7050932454964.7051.2523.22211580.00.2976330.16318LS+MCMC29.350.012820.782088.04500.531.339Claret (2011 A&A 529 75) ATLAS LS0.26020.4096q1_q17_dr25_koi
95601.739849133.0012702454966.0010.0433.114048.50.0063790.50770LS+MCMC0.720.029089.421608.01585.814.331Claret (2011 A&A 529 75) ATLAS LS0.28680.3588q1_q17_dr25_koi
95610.681402132.1817502454965.1820.1470.8650104.00.0094448.97692LS+MCMC1.070.015788.602218.05713.416.040Claret (2011 A&A 529 75) ATLAS LS0.30290.3239q1_q17_dr25_koi
9562333.486169153.6150102454986.6150.2143.1990639.00.02259085.88623LS+MCMC19.301.223389.98557.022.68796.000Claret (2011 A&A 529 75) ATLAS LS0.16970.5559q1_q17_dr25_koi
95634.856035135.9933002454968.9930.1343.078076.70.0080761.40645LS+MCMC1.050.060689.361266.0607.4212.060Claret (2011 A&A 529 75) ATLAS LS0.31380.2998q1_q17_dr25_koi